How Lovebirds Maneuver Rapidly Using Super-Fast

Transcription

CORRECTION
Correction: How Lovebirds Maneuver Rapidly
Using Super-Fast Head Saccades and Image
Feature Stabilization
The PLOS ONE Staff
Fig 8 is incorrectly cropped due to errors introduced during the typesetting process. The publisher apologizes for the error. Please see the corrected Fig 8 here.
OPEN ACCESS
Citation: The PLOS ONE Staff (2015) Correction:
Head Saccades and Image Feature Stabilization.
PLoS ONE 10(7): e0133341. doi:10.1371/journal.
pone.0133341
Published: July 24, 2015
Copyright: © 2015 The PLOS ONE Staff. This is an
open access article distributed under the terms of the
Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any
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credited.
PLOS ONE | DOI:10.1371/journal.pone.0133341 July 24, 2015
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Fig 8. Lovebirds improve visual flight control by coordinating super-fast gaze shifts with the end of their downstroke. (A) Top view schematic of a
typical recording that shows that the wings occlude the lateral visual field at the end of the downstroke. The lovebird’s azimuthal visual field is approximated
from ophthalmologic measurements at the visual equator of Senegal parrots [57]. (B) For demonstrative purposes a lovebird was filmed from the side during
a turning on a dime maneuver with the side panels of the arena removed (this video sequence is not part of the data analysis). (C) Most saccades were
initiated at 75% of the downstroke (see Fig 3B), when the wings occlude more than half of the lateral visual field.
doi:10.1371/journal.pone.0133341.g001
Reference
1.
Kress D, van Bokhorst E, Lentink D (2015) How Lovebirds Maneuver Rapidly Using Super-Fast Head
Saccades and Image Feature Stabilization. PLoS ONE 10(6): e0129287. doi:10.1371/journal.pone.
0129287 PMID: 26107413
PLOS ONE | DOI:10.1371/journal.pone.0133341 July 24, 2015
2/2
RESEARCH ARTICLE
How Lovebirds Maneuver Rapidly Using
Super-Fast Head Saccades and Image
Feature Stabilization
Daniel Kress1*, Evelien van Bokhorst1,2, David Lentink1,3
1 Department of Mechanical Engineering, Stanford University, Stanford, California, United States of
America, 2 Department of Mechanical Engineering and Aeronautics, City University London, London, United
Kingdom, 3 Experimental Zoology Group, Wageningen University, Wageningen, The Netherlands
* dkress@stanford.edu
Abstract
OPEN ACCESS
Citation: Kress D, van Bokhorst E, Lentink D (2015)
Head Saccades and Image Feature Stabilization.
PLoS ONE 10(6): e0129287. doi:10.1371/journal.
pone.0129287
Academic Editor: Doug Wylie, University of Alberta,
CANADA
Received: December 22, 2014
Accepted: May 6, 2015
Published: June 24, 2015
Copyright: © 2015 Kress et al. This is an open
access article distributed under the terms of the
Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any
medium, provided the original author and source are
credited.
Data Availability Statement: All high-speed tracking
data and further analysis data are available from the
Stanford digital repository database. The files are
available under http://purl.stanford.edu/wd014bk4606.
Diurnal flying animals such as birds depend primarily on vision to coordinate their flight path
during goal-directed flight tasks. To extract the spatial structure of the surrounding environment, birds are thought to use retinal image motion (optical flow) that is primarily induced by
motion of their head. It is unclear what gaze behaviors birds perform to support visuomotor
control during rapid maneuvering flight in which they continuously switch between flight
modes. To analyze this, we measured the gaze behavior of rapidly turning lovebirds in a
goal-directed task: take-off and fly away from a perch, turn on a dime, and fly back and land
on the same perch. High-speed flight recordings revealed that rapidly turning lovebirds perform a remarkable stereotypical gaze behavior with peak saccadic head turns up to 2700
degrees per second, as fast as insects, enabled by fast neck muscles. In between saccades, gaze orientation is held constant. By comparing saccade and wingbeat phase, we
find that these super-fast saccades are coordinated with the downstroke when the lateral visual field is occluded by the wings. Lovebirds thus maximize visual perception by overlying
behaviors that impair vision, which helps coordinate maneuvers. Before the turn, lovebirds
keep a high contrast edge in their visual midline. Similarly, before landing, the lovebirds stabilize the center of the perch in their visual midline. The perch on which the birds land
swings, like a branch in the wind, and we find that retinal size of the perch is the most parsimonious visual cue to initiate landing. Our observations show that rapidly maneuvering
birds use precisely timed stereotypic gaze behaviors consisting of rapid head turns and
frontal feature stabilization, which facilitates optical flow based flight control. Similar gaze
behaviors have been reported for visually navigating humans. This finding can inspire more
effective vision-based autopilots for drones.
Funding: Human Frontier Science Program, grant
RGP0003/2013, http://www.hfsp.org/ funded DK and
DL. The Office of Naval Research Multidisciplinary
University Research Initiatives Program, grant
N00014-10-1-0951, http://www.onr.navy.mil/ScienceTechnology/Directorates/office-research-discoveryinvention/Sponsored-Research/University-ResearchInitiatives/MURI.aspx funded DL. The funders had no
PLOS ONE | DOI:10.1371/journal.pone.0129287 June 24, 2015
1 / 24
Lovebird Gaze Behavior while Turning on a Dime
role in study design, data collection and analysis,
decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared
that no competing interests exist.
Introduction
When moving rapidly through a dense and cluttered environment like a forest, proximity information about tree trunks and branches is essential for negotiating a successful goal approach.
To accomplish such navigational tasks, diurnal birds rely on vision. Remarkably, little is known
about which visual cues birds actually use to orchestrate their rapid flight maneuvers during
close range navigation in their habitat. Well-studied visual depth perception mechanisms for
primate navigation and depth perception, in particular stereopsis, is very limited in birds and
therefore not considered to play a role in rapid flight control [1]. This motivated studies to discover alternative gaze strategies birds might use. Recent findings indicate that flying birds sense
retinal image motion, so called optic flow, to adjust their distance to walls, control their flight
speed, stabilize position during hovering and negotiate landing on a goal location, such as a
perch [2–6]. The way the bird’s eyes move through the environment defines how experienced
optic image flow of objects like trees and branches is composed. During straight, translational
flight optical image flow on the eyes is slow for distant objects and becomes faster as an object
gets in closer proximity. Consequently, experienced translational optic flow can be used to obtain relative distance information [7]. In contrast, optic flow during a rotation is distant independent. Its magnitude depends only on the velocity of eye rotation in space and is equal for
close and more distant objects [8]. Therefore, experienced rotational optic flow cannot be used
to extract reliable distance information. To increase the efficiency of extracting depth information from optic flow, birds use rapid head saccades to separate short-duration rotational from
long-duration translational motion, to compartmentalize eye motion in space [3].
Between rotational head saccades, head orientation is stabilized. Consequently, the time in
which purely translational optic flow, and thereby proximity information, can be obtained is
prolonged. Similar gaze behaviors have been reported for fast flying insects that rely on optic
flow for depth perception [9]. Birds that were prevented from stabilizing their heads during
flight inescapably crashed [10]. The findings that birds use optic flow to control flight were obtained by analyzing single modes of flight in isolation. Consequently, it is unknown how birds
switch between gaze behaviors and optic flow usage when performing a complete sequence of
navigational flight behaviors like take-off, turning and landing. Navigating humans for example tend to fixate the center of a goal that they want to approach while fixating the edges of an
obstacle they want to avoid in order to improve optic flow based steering [11,12]. To what extent do navigating birds perform similar gaze patterns as insects or humans?
We addressed the question of how birds change their gaze between maneuvering tasks by
analyzing the flight and gaze kinematics of lovebirds, a small generalist parrot, performing a
rapid turn maneuver during a simple goal directed task. Wild lovebirds roost and forage in
small flocks of 5–20 birds in the scrublands of central Africa and Madagascar. We investigated
the subsequent flight behaviors: take-off and fly away from a perch, turn on a dime, and fly
back and land on the same perch. Such a sequence resembles natural flight maneuvers regularly
conducted by this social bird when competing for perch locations on trees or feeding spots.
Based on the reports that about 85% of the total visually induced gaze shifts in birds are accomplished by head rotations [13,14] and eye movements are usually smaller than 10° in unrestrained birds [14–16], we approximated the lovebird’s gaze orientation during flight by
tracking the head orientation in a defined visual environment. The resulting data enabled us to
draw conclusions on conducted gaze behaviors and visual features used when maneuvering.
We find that maneuvering lovebirds conduct among the fastest gaze shifts so far reported
for vertebrates. These shifts are preferably timed at specific phases within a wingbeat and, thus,
are specifically adapted in their speed and occurrence to the requirements of visuomotor control of rapid flight.
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Methods
Birds and training
For flight recordings, we trained five lovebirds (agapornis roseicollis) to turn on a dime in our
custom-built flight arena (Fig 1A). The first step was to train the birds to fly between two perches.
In the second step, one perch was removed and birds were trained to fly away, turn and return to
the remaining perch. During the third step, the width of the perch was decreased to about 21 cm,
after which the birds were ready for the experiment. The birds were 2 years old at the time of the
experiments and their weights ranged between 47 g and 56 g (2LG, ♀, 54.8 g; 2DG, ♀, 53.8 g; 1Y,
♀, 47.1 g; 3Y, ♀, 55.6 g; 3G, ♂, 47.4 g). The air temperature during the experiments ranged between 20.8 and 21.6°C. The birds were housed in pairs in enriched cages in which they received
food and water ad libitum. For kinematic high-speed tracking, we painted waterproof ink marker
points (edding 750 paint marker, edding International GmbH, Ahrensburg, Germany) on specific parts of the bird’s head (Fig 1B). Depending on the bird’s plumage color, we chose either white
or black paint to maximize the image contrast of the applied marker points.
Experimental setup and procedure
Flight experiments were conducted in a custom-built arena measuring 0.6 m x 1 m x 0.95 m
(width x length x height; Fig 1A). The frame of the arena was made of aluminum and the wall
panels were made of transparent LEXAN sheets. We closed the top with wire mesh (19 x 19
Fig 1. Experimental apparatus and analysis techniques. (A) Box-shaped flight arena in which lovebirds performed a U-turn flight maneuver starting and
ending at the perch. The maneuver was filmed in stereo with two high-speed cameras at 2000 fps. (B) Illustration of the four time points within a wing beat in
which we assessed the head and body orientation in “low resolution” with respect to wing beat phase. Red dots depict marker points on the head used to
obtain its position and yaw orientation. Blue dots depict tracked shoulder positions used to obtain the yaw orientation of the body. (C) Schematic camera view
into the arena. Note that due to the indirect view via the mirror above the arena, camera images were mirrored around the horizontal axis. To avoid confusion,
we will refer to the flight scene as seen from the camera perspective. Red and blue lines show how yaw orientations of the head and body were obtained
relative to a horizontal line in the image.
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mm pitch, 1.4 mm thickness) to prevent birds from escaping. Two LED panels provided light
from above; each panel held an array of five equally-spaced (~23 cm pitch) ultra-bright LEDs
emitting 2650 lm ± 186 lm (BXRA-C2500, Bridgelux Inc., Livermore, CA, USA; Fig 1A). A
21.3 cm wide swinging perch was hung 11.5 cm away from the left side wall at a height of ~44
cm below the mesh. This perch, made of a 1.6 cm diameter PVC tube covered with sand paper,
constitutes the bird’s only takeoff and landing place within the arena.
One of the arena’s inner sidewalls was covered with a white cardboard sheet over the full
length. This white wall had a small grey textured rectangle measuring 25 cm x 20 cm positioned
at its center (Fig 1A). The arena floor was covered with plastic film to increase the birds’ contrast
on the video images. As the illuminated flight arena was placed in a completely darkened room,
all its walls except one sidewall appeared black to birds within it. Consequently, wall edges between the white and the dark walls constitute features with strong contrast within the arena. To
film the flying bird from above, two laterally placed high-speed cameras (Photron APX and PCI
camera, Photron Inc, San Diego, CA, USA) were directed at an inclined mirror above the arena
(Fig 1A). For orientation reference: in the following sections, we will describe our findings based
on the recorded camera images which are mirrored around the horizontal axis—thus right and
left arena sides are switched. Consequently, when mentioning the white sidewall appearing left
in our figures (Figs 1C and 2A), we are actually describing the right arena sidewall (see Fig 1A &
1C for clarification). The same holds for turning directions (S1 Movie).
To initiate the turn on a dime flight, the light was turned on and a hand wave cue was given
when the bird was adjusted to the light level. Lights in the flight arena were turned off in between recordings to let the bird rest and discourage preening. During this time, the recorded
flight videos from the two high-speed cameras were stored to disk.
Video analysis
Low resolution wingbeat related tracking & 3D marker reconstruction. Flight behaviors
were filmed with two synchronized cameras at 2000 frames per second and a resolution of
1024 x 512 pixels (S1 Movie). To obtain the marker points’ 3D position in the filmed volume,
we used a stereo triangulation procedure. Marker tracking and 3D marker reconstruction was
done with the “DLTdv5” toolbox [17] for MATLAB (The MathWorks, Natick, MA, USA). 3D
camera calibrations were created with the “easyWand” toolbox for MATLAB [18]. Each flight
recording lasted about two seconds, which resulted in about 4000 recorded frames per camera
and flight. As the manual marker tracking would have taken a single person about four years to
complete for all recordings, we had to reduce the tracking procedure in a reasonable way. Bird
flight dynamics depend on the kinematics of their wingbeat [10,17,19,20]. Therefore, we decided to track head marker points and shoulder positions only at four specific instances within a
wingbeat, which enabled us to perform wingbeat related tracking. A complete wingbeat is defined by a complete down- and an upstroke of the wings. We noted the start and end times of a
single wingbeat manually by going through the flight recordings frame by frame. We marked a
frame as start of the downstroke (end of the upstroke) when the wings were in their most dorsal
position. Similarly, we marked a frame as end of the downstroke (start of the upstroke) when
the wings were in their most ventral position. Beside the start and end points, we also marked
the two frames half way through an up and downstroke, respectively (Fig 1B). Maneuvering
lovebirds changed their wing beat frequency throughout the recorded flight. Consequently, the
wingbeat related tracking frequency varied as well and ranged from about 35 to 70Hz (four
times the wingbeat frequency).
High resolution tracking. One of our main interests in this study was to analyze the gaze
behavior of maneuvering birds. The temporal resolution of the wingbeat related tracking
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Fig 2. Individual example of a turning on a dime maneuver. (A) Head trajectory during a leftward U-turn
flight maneuver in the flight arena. Plotted head position (gray dots) and head yaw orientation (red lines) are
presented in 25 ms steps (50 frames). Because the turn is performed on a dime, traces after takeoff and
before landing overlap. Arena wall grayscale (black, gray, and white) illustrates the inner wall texture seen by
the bird. The thick black bar in the arena shows the perch position. (B) Top view of all 15 wing-tip positions at
mid stroke during the same turn maneuver as shown in (A). Deflections in wing tip traces (wing beat 5–6 &
11–12) were used to separate the continuous flight maneuver into three consecutive phases: before turn
(gray), during turn (blue) and after turn (red). (C) Head (red) and body (blue) yaw orientation angle (Φ) during
a left turn maneuver. Φ is calculated in relation to a horizontal axis in the flight arena (see Fig 1 and methods).
Φ values of 0° indicate an orientation along the horizontal axis facing away from the perch whereas Φ of 180°
indicates an orientation along this axis facing towards the perch. Positive Φ deflections indicate a turn to the
left, negative ones a turn to the right. The Φ difference angle between head and body is shown in green. Body
data was filtered and interpolated (blue dashed line, see methods) to calculate the Φ head-body angle.
Vertical bars represent the downstroke phases and thereby the wing beat timing before, during and after the
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turn. (D) Saccade detection (black, see text) based on absolute yaw rotation velocity (ω, gray graph). The
dashed gray line illustrates the saccade detection threshold of 400°/s that ω had to exceed for at least 12 ms
(24 data points) to define a head turn as saccadic.
procedure was not sufficient to analyze head and thereby gaze kinematics in detail. Therefore,
we analyzed head data in multiple flights (at least three per bird), for each bird at full temporal
resolution of 2000 Hz. The selection of flights was made based on following criteria: (i) the
birds had to be in the camera view from beginning to the end of the flight. (ii) The flight contained not more than one complete U-turn. (iii) Changes in its flight altitude were less than 21
cm. (iv) Birds were facing into the flight direction (± 45°) before taking off. (v) Landing on the
perch was successful on the first attempt, and to optimize marker tracking, (vi) head makers
were not covered by the wires of the top mesh for long flight passages. 16 flight recordings of
five birds fulfilled these criteria. Automated marker tracking for these recording was done with
the open source software “ivTrace” (http://opensource.cit-ec.de/projects/ivtools) [21–23]. We
call the tracking procedure at full temporal resolution “high resolution tracking”.
Data filtering. Due to marker coverage by the wires of the top mesh and due to varying
marker contrast levels for the differently colored birds, tracking could not be done completely
automatically but was interspersed by sections in which we tracked manually. To account for
noise introduced by our tracking procedures and digital jitter, we filtered the obtained marker
point coordinates and obtained yaw orientations with a penalized least-squares smoothing algorithm based on a Whittaker filter [24,25]. This filter algorithm is in its function similar to a
Savitzky-Golay filter but much more resource efficient [24]. The smoothing parameter λ was
conservatively chosen to result in cross-validation errors (cve) to the original data below two
(λcoordinates = 100, cvecoordinates < 0.5; λhead = 5000, cvehead < 1; λbody = 1000000, cvebody < 2).
After 3D reconstruction of marker point and shoulder positions, we calculated the head and
body yaw orientations for each time step. Their yaw orientation F was defined as the arctangent of the vector connecting both head makers (Fhead) and of the normal of the vector connecting the birds’ shoulders (Fbody) (Fig 1C). As body data was obtained at the lower, wing
beat related temporal resolution, we filtered it with a Whittaker filter (see methods) and interpolated the data up to 2000 Hz to obtain a highly resolved F difference angle between head
and body. The total error in 3D position estimation was on average 1.3 mm ± 0.4 mm. This
value was based on the 3D reconstruction and calibration errors averaged across all tracked
points and birds. The resulting orientation error is about 3.8°.
Similar to earlier studies addressing rotational head movements in free behaving animals
[3,21], we defined head yaw saccades based on rotations exceeding a threshold speed for a certain minimal duration. Rotation velocities (ω) were obtained by taking the derivative from the
filtered yaw orientation data. For our data, a rotational velocity of 400°/s and a duration of 12
ms (i.e. 24 consecutive frames) turned out to reliably detect head saccades. By comparing the
onset of rotational velocity with the point in time when it exceeded 400°/s, we derived that saccade durations had an uncertainty of about 4 ms.
Data sets for the quantitative data analysis. The quantitative saccade and landing analyses were based on high-resolution tracking data (2000 Hz) consisting of 3–4 flights for each of
the five tested birds (see classification criteria above). To avoid strong effects of ascending or
descending maneuvers and have better comparable flights, we only analyzed turn flights for the
wingbeat analysis where height changes during the turn were minimal (< 21 cm) resulting in
9–10 flights per bird. Quantitative analysis of gaze orientation was done first with the smaller
high-resolution data set, and after validating that found distributions are qualitatively similar,
the analysis was extended to the larger low-resolution wingbeat related data set that included
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all above mentioned flights. This set contained 12–26 flights per birds. In all analyses, we averaged the results per bird before comparing or averaging across birds.
Analysis of wingbeat and stroke period distributions. Wingbeat and downstroke / upstroke periods were analyzed for bimodal distributions using the Gaussian mixture models algorithm (GMM) of the MATLAB statistics toolbox. The GMM algorithm assumes that the
wing data are a mixture distribution, where the probability density function is a combination
with coefficients that sum to 1 (ξ1 + ξ2 = 1),
f ðxÞ ¼ x1N1 ðm1; s1Þ þ x2 N2 ðm2; s2Þ;
1
where μ1 and μ2 are the distribution means, σ1 and σ2 the standard deviations and ξ1 and ξ2 the
mode weights. A fitted distribution was categorized as bimodal if the following conditions were
true:
jm1 mj > 2 max ðs1; s2Þ
and when
min ðx1; x2Þ > 0:1
Advance ratio. The advance ratio J was calculated for each wingbeat:
J¼
V
;
2AfR
2
where V is the wingbeat averaged flight speed in m/s obtained from taking the derivative of
head position data, A is the wingbeat amplitude in radians obtained from calculating the angles
between most dorsal and most ventral wing tip position divided by 2f, the wingbeat frequency
in Hz and R, the root-to-tip wing length [26]. The root-to-tip radii ranged between 12.62 cm to
14.03 cm for the five tested birds.
Ethics statement
All training and experimental procedures were approved by the Institutional Animal Care and
Use Committee of Wageningen University (DEC; Dierexperimentencommissie; protocol
2011095.b; in accordance with 86/609/EEC). Birds were housed in pairs in enriched cages,
which included toys and artificial full-spectrum lights.
Results
To investigate the saccadic gaze behavior and visual cues that maneuvering lovebirds use, we
analyzed turning flights of five individuals. In all recordings, we observed a very stereotypical
turning behavior in which the head initiated the turn with saccades, while the body followed
with a slower but more constant turning behavior. Remarkably, in 90% of the analyzed flights
(n = 92 flights, N = 5 birds) lovebirds turned left towards the white sidewall. Only 10% of the
analyzed flights contained right turns towards the dark sidewall (n = 10 flights, N = 3 birds, see
wall configuration in Fig 1C). Apparently, lovebirds preferred to turn towards high contrast
features in the arena when performing a U-turn maneuver. Therefore, we will focus our quantitative analysis on left turning flights for which we obtained a more representative sample size.
To give insight into how an actual recorded U-turn flight looks, we will illustrate one example first (Fig 2, S1 Movie) before presenting quantitative results across birds (Figs 3 to 7). In a
typical recorded flight, the lovebird took off from the perch, flew more than halfway into the
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arena, performed a rapid turning maneuver ‘on a dime’ in which it oriented itself back to the
perch, flew back to the perch and landed on it (Fig 2A).
We divided the U-turn flight into three segments to analyze each phase individually: (i) the
straight flight phase after take-off and before the turning maneuver (before turn), (ii) the turning phase (during turn) and (iii) the straight flight phase after the turn until landing (after
turn). The segmentation was based on the position of the wingtips during mid-stroke. When
the turn was initiated, the tip position changed characteristically by deviating from its previously relative straight trace (Fig 2B: transition gray to blue bars). The phase after turning
started when the wingtip positions were again aligned along a straight trace (Fig 2B: transition
blue to red phase). Throughout the recorded flights, wingbeat duration varied as indicated by
the differences in down- and upstroke time (Fig 2C, S1 Movie), showing that lovebirds perform
intermitted flight.
Maneuvering lovebirds had a relative constant head orientation before and after the turn
(Fig 2C, gray and red bars). However, during the turn the birds changed their head orientation
in a saccadic fashion; during short bursts they turned their head very quickly into the turning
direction while keeping head orientation stable in between (Fig 2C, blue phase). Head saccades
were typically faster than 1000°/s, and higher speeds beyond 2000°/s were also observed, as illustrated by the second saccade in Fig 2D. While we could track head data automatically at
2000 fps, we obtained body data manually at four times the wingbeat frequency (at about 68
Hz; see methods), and fitted them with a penalized least-squares smoothing algorithm for noncontinuous data [24]. In contrast to the head, the body turned smoother and slower during the
turn, following the head without saccades (Fig 2C, blue line, S1 Movie). Consequently, head
and body orientation deviated during head saccades and realigned again during intersaccadic
phases (Fig 2C, green line).
Wing beat kinematics & gaze behavior
Due to their visuomotor capabilities, birds are model organisms in the fields of animal locomotion and visual information processing. However, not much is known about how flight kinematics and visual behaviors are tuned to work in concert. When analyzing wingbeat
kinematics and gaze behavior across all five birds we found that: (i) the wingbeat of lovebirds is
intermittent during maneuvering flight, and (ii) saccadic gaze shifts are preferably initiated at
the end of the downstroke.
Wingbeat & flight kinematics. We analyzed the wingbeat kinematic data with a Gaussian
mixture model to determine if they are bi-modally distributed due to intermittency. The resulting Gaussian fits reveal that both the flapping frequency and the relative periods of the downand upstrokes are bimodal distributed for each individual bird. Lovebirds almost halved their
normal flapping frequency (17.01 Hz ± 0.87 Hz, mean ± S.D.) during intermittent flaps (9.58
Hz ± 0.48 Hz, mean ± S.D.; Eq 1; Fig 3A). They accomplished this frequency reduction by extending the upstroke duration to twice the time of the downstroke. During an intermittent flap,
the ratio of down- to upstroke period was 0.53 ± 0.06 and the wingbeat was performed at about
9.5 Hz. In contrast, normal wingbeats were performed at approximately 17 Hz, and the downto upstroke ratio averaged at 1.35 ± 0.11. The downstroke is thus 35% longer than the upstroke.
To see whether the flapping intermittency is modulated between phases of the U-turn, we analyzed the percentage of intermittent vs. normal frequency wingbeats in each phase, for each individual bird, and averaged the results. There is no general difference in intermittency between
phases (p = 0.074, Friedman test with Bonferroni correction, n = 697 wingbeats) but intermittent flaps (n = 153) tended to happen most often in the phase after the turn, while normal flaps
(n = 528) tended to occur predominantly during the turn. Of all recorded intermittent flaps,
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Fig 3. Lovebirds performed an intermittent flight style with two wingbeat distributions in which
saccades are started during downstrokes. (A) The downstroke / upstroke phase ratio vs. instantaneous
flap frequency distribution for individual wingbeats of five birds. A phase ratio of 1 indicates up- and
downstrokes of equal duration, values <1 indicate longer upstrokes, values >1 longer downstrokes.
Normalized bimodal Gaussian fits are shown for flap frequency (top) and for downstroke / upstroke time ratios
(right). The bird-specific bimodal distribution parameters for the flapping frequency are: 2dg: μ1 = 9.78, σ1 =
1.61, μ2 = 17.26, σ2 = 1.01; 2lg: μ1 = 10.26, σ1 = 1.83, μ2 = 18.14, σ2 = 0.91; 2y: μ1 = 9.39, σ1 = 1.1, μ2 = 17.19,
σ2 = 0.86; 1y: μ1 = 8.97, σ1 = 0.6, μ2 = 15.76, σ2 = 0.96; 3g: μ1 = 9.49, σ1 = 2.3, μ2 = 16.72, σ2 = 1; For
downstroke / upstroke periods the obtained bimodal distribution parameters are: 2dg: μ1 = 0.5, σ1 = 0.07, μ2 =
1.26, σ2 = 0.27; 2lg: μ1 = 0.56, σ1 = 0.13, μ2 = 1.43, σ2 = 0.17; 2y: μ1 = 0.48, σ1 = 0.07, μ2 = 1.26, σ2 = 0.27; 1y:
μ1 = 0.62, σ1 = 0.09, μ2 = 1.49, σ2 = 0.17; 3g: μ1 = 0.48, σ1 = 0.12, μ2 = 1.3, σ2 = 0.17. The horizontal gray line
separates the bimodal distributions at a downstroke / upstroke ratio of 0.94 (average midpoint between
bimodal distribution peaks among birds). The vertical gray line separates the bimodal distribution at a flap
frequency of 13.3 Hz (average among birds); n = 697 wing beats, N = 5 birds. Due to the 2000 fps sample
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frequency, and the fact that wingbeat, downstroke, and upstroke time are all integer values measured in
number of frames, the data appear in a raster and can overlap precisely among wings beats, flights and birds.
(B) The normalized saccade distributions illustrate when a saccade was started and ended during the
downstroke vs. the upstroke phase. Shown is the average across birds (solid lines) and the standard
deviation (shaded area). Binning: 0:10:100; n = 72 saccades, N = 5 birds.
26.5% ± 12% occurred in the phase before the turn, 29.8% ± 9.9% during the turn and 43.7% ±
13.14% after the turn across birds. Of all normal flaps 30.8% ± 5% occurred before the turn,
40.9% ± 5.2% during and 28.7% ± 3.6% after the turn.
To see if flight speed varied between flight phases, we calculated the average flight speed per
wingbeat and averaged it for each bird over each flight phase. Lovebirds flew 0.54 m/s ± 0.01
m/s slower during the turning phase than during the phase before the turn (1.05 m/s ± 0.16 m/
s) and the phase after the turn (1.03 m/s ± 0.16 m/s; p = 0.02, Friedman test with Bonferroni
correction, n = 697 wingbeats). The advance ratio J, is a non-dimensional parameter defined as
the ratio between forwards flight speed and the mean wingtip velocity (Eq 2), it indicates if an
animal is in slow hovering flight or forward flight [26]. In accordance to the wingbeat average
flight speed, the advance ratio was significantly reduced in the turning phase (0.056 ± 0.01) as
compared to the straight flight phase before the turn (0.12 ± 0.03) and the straight flight phase
after the turn (0.11 ± 0.01; p = 0.02, Friedman test with Bonferroni correction, n = 697 wingbeats). This quantitative result confirms the general observation that rapid turning lovebirds
were in slow-hovering flight throughout the maneuver [26].
Coordination of gaze with wingbeat kinematics. Are gaze shifts in the form of superfast
head saccades related to wingbeat kinematics? As apparent from scrutinizing the saccadic gaze
shift timing in the individual example (Fig 2D, black trace, S1 Movie), the initiation of a head
saccade was performed specifically during downstroke phases of the wingbeat. To analyze this
observation more quantitatively, we noted the relative point in time of saccade initiation for
the downstroke and for the upstroke phases of a wingbeat. From the 16 flights in which we analyzed head motion at 2000 Hz, 66 of 72 detected head saccades were made during the downstroke (Fig 3B, green distribution). Saccade probability was highest in the last third of the
downstroke. Only six saccades were made during the upstroke phase (right after the start). The
duration of a saccade was typically half a wingbeat (see head kinematics section below). Saccades that started during the downstroke were, therefore, usually completed during the upstroke (Fig 3B, red distribution). Some saccades lasted into the next wingbeat and ended in the
first half of the downstroke. The differing shapes of start and end distributions point out that
head saccades were variable, which we show quantitatively below.
Head saccade kinematics
In contrast to humans, birds shift their gaze mainly through head reorientation (review: [14]).
Head saccades can therefore be analyzed to assess gaze performance and strategies. We found
that lovebirds performed head saccades more frequently and faster during turning flight. Combined, head saccades made up 76% of the total head reorientation during the turn. Saccade velocities reached values of up to 2700 °/s and are thus, in terms of speed, comparable to head
saccades in insects which are three orders of magnitude lighter (Fig 4). Although the head saccade amplitudes are comparable with eye saccade amplitudes of humans and rabbits, lovebirds
perform head saccades about three times faster (Fig 5; based on time-resolved tracking data at
2000 Hz). When plotting saccade start position over the flight trajectory, we found that lovebirds make most saccades during the turning phase (Fig 4A, circular markers). Saccades during
the turn were typically faster than saccades before or after the turn (see color code in Fig 4A).
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Fig 4. Head saccades occur predominantly during the turn and their speed compares to insects. (A) Flight traces of all birds depicting the position of
saccade initiations during a U-turn. Turning flight traces are shown as grey lines, saccades as red colored symbols: triangles depict saccades made before or
after the turning phase, cycles illustrate saccades during the turning phase. The dashed red line in the color bar represents the saccade detection threshold
of 400°/s. The perch position is represented by the vertical black bar. A rare right turn flight of bird 2y is illustrated for representative reasons by the thicker
line, n = 16 flights, N = 5 birds. Panels (B-F) illustrate differing head kinematics during intersaccades and saccades as well as the extraordinarily fast nature of
lovebird head saccades. The shaded areas illustrate the standard deviation between birds. (B) Amplitudes of horizontal head saccades. Shown are the
normalized absolute saccade amplitude distributions relative to a horizontal axis through the flight arena and the normalized relative saccade amplitude
relative to the body yaw orientation. Binning = 0:10:60. (C) Normalized average of saccade duration across birds. Binning = 0:10:50. (D) Normalized average
head yaw rotation speed in space for saccades and phases between saccades (intersaccades). Binning = 0:100:2000. (E) Normalized average head yaw
rotation speed relative to the turning body, plotted for both saccades and intersaccades. Binning = 0:100:2000 (see text for definition). (F) Peak saccadic
rotation speeds of the head. To compare the performance of lovebirds to other flying animals showing similar saccadic gaze behaviors, we inserted reported
saccadic peak rotation speeds of the honey bee [28], zebra finch [3], the blowfly [27] and the fruitfly [29]. Binning = 0:300:3000. n = 72 saccades, N = 5.
Saccade analysis is based on video data resolved at 2000 Hz.
To compare saccade parameters between birds, we averaged the saccades over individual flights
for each bird, before calculating the average across birds. In general, head yaw saccades reached
amplitudes up to 60° with a median of 27.7° across birds (Fig 4B). Remarkably, head saccade
amplitudes with respect to the arena were comparable to head saccade amplitudes with respect
to body yaw orientation (p = 0.96: Friedman test with Bonferroni correction). This shows that
head saccade amplitude was not substantially modified by body rotation. Saccade duration varied and had a median at 29.6 ms. The longest recorded saccade lasted 44.5 ms (Fig 4C). While
the median head saccade velocity was 926.1°/s across turning birds, rotational head velocities
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were dramatically reduced during intersaccades to 146.4°/s (Fig 4D). We quantified head yaw
velocities relative to the arena and relative to the body (Fig 2C, green line) and averaged them
over both the saccadic and intersaccadic phases (Fig 4E). Head yaw velocities with respect to
the body were higher between head saccades than absolute yaw velocities with respect to the
arena (compare red distributions Fig 4D and 4E; p = 0.03, Friedman test). This shows the extent
to which the head was stabilized during intersaccades. In contrast, during head saccades, the
head yaw velocity with respect to the body was lower than the yaw velocity with respect to the
arena, because the head and body turned in the same direction (compare blue distributions Fig
4D and 4E; p = 0.025, Friedman test). When plotting the distribution of peak saccade velocities
with respect to the arena it becomes obvious that most head saccades exceeded velocities over
1000°/s (median 1290°/s). The highest measured head saccade velocity was 2700°/s. This value
reaches almost head turning speeds achieved by flying blowflies [27], 1000 times lighter than
lovebirds, and it exceeds head turn speeds reported for honey bees [28]. This is even more remarkable considering lovebirds are four times heavier than zebra finches, which they also outperform [3]. The fastest in-flight saccade speed has been reported for fruitflies, performing
saccadic body turns which reach 5400°/s during escape maneuvers [29]. Whereas these metrics
are impressive, we wondered how saccade amplitude and duration compares across vertebrates.
To determine how saccade amplitude is related to its duration, we plotted the saccade amplitude as a function of the saccade duration (Fig 5A). We found a strong correlation between
the amplitude and the duration of a saccade (r = 0.88). The data can be approximated with a
linear regression (y = 1.5 x-14, slope = 1500°/s, R2 = 0.78), suggesting that lovebirds have a preferred average head saccade velocity of 1500°/s. To evaluate this finding in a broader context,
we extracted saccade amplitude and duration data from figures of earlier studies investigating
gaze behavior in visual model organisms, specifically vertebrates (Fig 5A, [30–33]; the open
source software ImageJ was used for data extraction http://imagej.nih.gov/ij/index.html). Compared to the combined eye-head gaze shifts of rhesus monkeys, and the eye saccades of humans, rabbits and cats, the saccadic gaze shifts of maneuvering lovebirds are extraordinarily
fast. There is a general trend that saccades in lovebirds are shorter than in others species,
Fig 5. Lovebird head rotation is predominantly saccadic at the highest average speed recorded amongst vertebrates. (A) Comparison of horizontal
saccade amplitude as a function of its duration. Shown are saccade amplitudes and durations measured in this study (blue triangles, n = 72, N = 5) and data
for other species extracted from earlier publications. Gray triangle markers represent the combined eye-head gaze shifts of rhesus macaques (n = 544,
N = 2) [30]. Red, green and violet circles illustrate horizontal eye saccades in humans (n = 187, N = 3) [31], rabbits (n = 191, N = 2) [33] and cats (n = 34,
N = 2) [32]. We coarsely approximate average head rotation velocities by fitting the data with a linear regression. Line equations: lovebird: y = 1.5*x-14,
slope = 1500°/s, R2 = 0.78; rhesus macaque y = 0.4*x+3.4, slope = 400°/s, R2 = 0.69; human: 0.38*x+3.4, slope = 380°/s, R2 = 0.93; rabbit: y = 0.33*x-10,
slope = 260°/s, R2 = 0.67, cat: y = 0.11*x-4.2, slope = 110°/s, R2 = 0.81. (B) Proportion of saccadic turns on the whole U-turn maneuver. Shown is the
average cumulative saccade amplitude (and standard deviation) as a percentage of the whole turn amplitude, with the red line showing the average across
birds (n = 72 saccades, N = 5 birds).
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although their amplitudes are comparable to eye saccades in humans and rabbits. Horizontal
head saccades in lovebirds are much larger than eye saccades of the cat. Combined gaze shifts
in rhesus monkeys (head + eye) reach higher amplitudes than lovebird head saccades. The variability in saccade amplitude stimulated us to determine how much saccadic vs. intersaccadic
gaze changes contribute to the total orientation of the head during a U-Turn. Hence, we
summed all saccade amplitudes and compared it to the total head reorientation during the
turn. We found that, on average, three quarters of the horizontal head reorientation was accomplished through saccadic gaze changes (76.25% ± 3%; Fig 5B). This value was similar in all
birds (p = 0.62, Kruskal-Wallis test), showing that gaze changes were predominantly facilitated
by super-fast head saccades, but towards which arena features?
Gaze orientation during flight
Due to their highly flexible necks and relatively light heads, birds facilitate large gaze shifts primarily by fast head turns. The fast head saccades we report here can be considered as fast gaze
shifts, because the relative eye movements in unrestrained birds are less than about 10° [14].
Like in humans, visual perception in birds is suppressed during fast head and eye saccades
[34,35]. Hence we wondered which features lovebirds keep stabilized in their visual field
between saccades.
By combining head orientation and flight arena geometry (Fig 6A), we found that maneuvering lovebirds stabilize high contrast features in their frontal visual field during intersaccades.
Therefore, we indicate the range of feasible eye movements, which results in an uncertainty in
the estimated gaze direction in the frontal visual field, using a gray horizontal bar in Fig 6B and
Fig 6C–6K. Typical gaze shifts (based on Fig 2) are shown in Fig 6B. To analyze gaze distributions quantitatively, we averaged retinal feature positions across birds (n = 92, N = 5) [21]. Due
to motion blur [36] and saccadic suppression [34,35], we focused our quantitative gaze analysis
on intersaccadic phases for which head orientation is relatively constant (Figs 2D and 4D). We
investigated these phases across 92 left turn flights (including the 15 left turns resolved at 2000
Hz) for which we then tracked the head at four times the wingbeat frequency (about 68 Hz).
To separate saccadic from intersaccadic phases in this larger data set, Fig 4A, we excluded head
yaw data which exceeded a standard deviation of 10° yaw per wingbeat (± 10°: gray shaded bar
in Fig 6B–6K, [13–15]). This value is based on the largest horizontal eye movements that may
assist in stabilizing gaze. Azimuthal angle distributions were independent of acquisition rate
(correlation coefficients for compared arena features > 0.9). Hence, lower temporal resolution
wingbeat data can be used robustly to determine intersaccadic head orientation.
During the flight phase before the left turn, the lovebirds’ head was directed at the center of
the high contrast corner, formed by the white sidewall and the dark headwall. This high contrast edge was thus centered in the middle of their visual field (Fig 6A and 6B violet line; Fig 6C
dotted line). During the left turn, lovebirds shifted the relative position of arena features multiple times to the right by saccadic head turns to the left (Fig 6B). The magnitude of these shifts
depends on the amplitude of the yaw saccades (compare Fig 6B with 2D). While turning, the
majority of birds first centered the left high-contrast edge of the white sidewall (Fig 6A and 6B
light green line; Fig 6D). During intersaccades they faced the right edge of the perch (Fig 6A
and 6B dark green line; Fig 6E). After turning, the majority of flights ended with a saccadic
gaze shift to center the perch and stabilized it frontally (Fig 6A and 6B green line; Fig 6F). As a
consequence of frontal feature stabilization (Fig 6G–6K), other adjacent features have lower
laterally positioned probability peaks (compare after turn distributions of Fig 6F with Fig 6E
and 6K). For the above described gaze analysis, the position of the perch in rest was used, despite the fact that the perch was swinging forward and backward (amplitude ~10 cm; sideward
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Fig 6. Distributions of intersaccadic azimuthal feature positions reveal that maneuvering birds stabilize arena features in their frontal visual field.
(A) Schematic top view into the arena. Azimuthal positions of wall corners, the gray square and the perch (colored lines) were obtained relative to the bird’s
head yaw orientation (red line). (B) Relative azimuthal angles of arena features for the example flight shown in Fig 2. The thick green line depicts the angle of
the perch center. The gray shaded area represents feasible horizontal eye motions of ±10° relative to the horizontal head orientation. Positive azimuthal
angles represent the left visual hemisphere, negative values the right visual hemisphere (see bird illustration above legend). Note that the diverging
azimuthal angles of the perch edges (dark green and blue lines) are caused by the bird getting closer to the perch. Approaching the perch causes the retinal
size of the perch to expand in the bird’s frontal visual field. (C-F) Averaged relative azimuthal distributions of arena features that were stabilized in the frontal
visual field in the intersaccadic phases during a turning on a dime maneuver; before the turn (C: fine dashed line), during the turn (D & E: solid line) and after
turning (F: coarse dashed line). Averaged distributions illustrate left turn flights (n = 92, N = 5). Standard deviations across birds are illustrated by the colored
areas. (G-K) Intersaccadic azimuthal distributions for arena features that were not stabilized in the frontal visual field. By stabilizing the perch center frontally
after the turn (F), the right and left edge distributions are positioned more laterally, are broader and have lower peaks than the center (E & K). The vertical bar
extending ±10° illustrates feasible horizontal eye motions relative to the head orientation in unrestricted birds (review: [14]). Perch position for 92 flights is
approximated by using the position of a static perch (thick lines in panel E-F, n = 92 flights, N = 5). For 15 left-turn-flights tracked at 2000 Hz, the position of
the swinging perch relative to the birds was tracked as well (gray dashed line in panel E, F & K n = 15 flights, N = 5). For normalization, we divided each
distribution by the cumulative sum of all other feature distributions. Binning = -90:10:90.
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amplitude ~1 cm). We checked whether this perch motion changes our conclusions, by calculating the actual perch azimuthal position with respect to the bird for the higher resolved data
set (n = 15 left turn flights, N = 5). We found that the results were quantitatively similar for a
static and a moving perch (dashed lines in Fig 6E, 6F and 6K). Birds encounter moving perches
in their environment when landing on swaying branches and power lines in wind, so which visual cues do birds use to coordinate landing on moving perches?
Visual cues for coordinating landings on a swinging perch
Studies in the past have shown that birds and insects use visual cues during the landing approach, such as the azimuthal retinal extent of the landing site (α), its relative retinal expansion
velocity (RREV), and the estimated time-to-collision (tau), to initiate landing behavior on a
static spot (houseflies: [37,38]; fruitflies: [39]; bees: [40]; pigeons: [4,5]; hawk: [5]). To obtain
the RREV and tau birds rely on the experienced α and its rate of change over time, the experienced retinal expansion speed (O). We obtained α by calculating the difference angle between
the azimuthal angles for the right and left perch edges for every recorded frame (Fig 6A). We
estimated O from taking the derivative of α. We calculated RREV as the ratio O/α and tau as
the ratio α/O [39].
To evaluate which of the mentioned parameters are likely used by lovebirds to control landing on a swinging perch, we applied a coefficient of variance (c.v.) analysis introduced by Wagner for landing houseflies [37] and subsequently used for landing pigeons and a hawk by Green
and Davies [5]. The general assumption is that the parameter varying least across individuals
and trials before landing initiation is the parameter most likely (parsimonious) to trigger landing behavior. The c.v. for each optical parameter was calculated by dividing their standard deviation across trials by their means across trials, for each time step before landing initiation. In
our case, we defined the point in time of downward pitch of the tail feathers as the indicator for
landing initiation. Bird’s visuomotor delay is in the range of 30 ms to 70 ms [41,42], we therefore started analyzing control parameters 85 ms before the down pitch of the tail feathers.
The dynamic swinging motion of the perch induced large variations in the relative approach
speed (compare Fig 7A & 7B). We found that in all cases α varied least (Fig 7C). This low variation indicates that α is the most parsimonious to trigger landing behavior in lovebirds landing
on a moving perch, such as a branch swaying in the wind.
Discussion and Conclusions
Lovebirds performing a rapid ‘turn on a dime’ maneuver shift their gaze between arena features
with superfast head saccades. These gaze shifts reach on average 930°/s with peaks up to 2690°/
s, the fastest recorded saccades for vertebrates to date [14,36] (Fig 5A). This speed compares to
the saccade speeds reported for flying insects that are up to three orders of magnitudes lighter
than lovebirds [27] (Fig 4F). These extraordinarily fast turns are made possible by the highly
specialized avian neck system, of which the muscles contract effectively as fast as the flight
muscles [43]. Between saccades, lovebirds stabilize their head towards prominent arena features in the frontal visual field (Fig 6). Compared to the head, body rotations are continuous
and slower, lacking the characteristics of saccades (Fig 2C, S1 Movie). This finding indicates
that flying lovebirds perform a gaze strategy that facilitates optic flow processing [3,44,45]:
They are separating distance dependent translational optic flow from distant independent rotational optic flow cues at the behavioral level. This optic flow orchestration simplifies neural
processing [8,45] in visual brain centers [46–50], which facilitates the extraction of relative
proximity and heading information. A similar head turn behavior has been reported for turning pigeons [51]. However, due to the different focus of their study, Bilo and colleagues
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Fig 7. Low variation of retinal size and relative expansion velocity (RREV) of the approached perch
suggests these cues matter for controlled landings on a swinging perch. We defined the tail pitch as the
behavioral indicator for landing initiation (time = 0 ms). Negative time values represent the time before and
positive time values the time after the downward pitch of the tail feathers. Shaded areas ranging from -30 ms
to 0 ms mark the minimal time period of visuomotor delay during which visual flight control is unlikely.
Absolute horizontal flight speed (A) has less variation across flights than relative horizontal flight speed (B)
with respect to the moving perch. (C) The most parsimonious landing parameters are indicated by a minimum
in the coefficient of variation (c.v.) across flights and birds. The retinal size (orange) and RREV (green) for the
approached perch varied less that the parameter tau and the retinal expansion. Tail pitch timing was
extracted individually from high-speed flight videos. n = 16, N = 5 birds.
interpreted their findings mainly within the framework of reflexive neck and wing control
mechanisms [51]. Our finding that lovebirds landing on a moving perch experience low variation in retinal perch size, supports the hypothesis that landing birds use this cue for visual flight
control, in addition to self-motion related parameters [5]. Lovebirds shift their gaze between
arena features using super-fast saccades that are timed with the last quarter of the wings’ downstroke (Fig 3B). As their wings occlude lateral vision during this stroke phase, lovebirds maximize visual perception by overlying behaviors that impair vision (Fig 8). In flying bats a similar
coupling between wing kinematics and acoustic sensing was found. Their respiration and ultrasonic calls are related to their wingbeat frequency [52–54]. By analyzing the wingbeat frequency of maneuvering lovebirds, we find them to be intermittent as reported for other small
generalist birds [55,56]. Interestingly, lovebirds compose their wingbeat of two distinct flapping
modes (Fig 3A). During their slower flapping mode of 9.5 Hz, upstrokes are twice as long as
downstrokes while downstrokes are slightly longer in the normal flapping mode of 17 Hz.
When turning, lovebirds perform preferably normal wingbeats and, accordingly, most observed saccadic gaze shift are coordinated within this flapping mode.
Super-fast gaze changes
When comparing lovebird saccadic gaze shifts with the ones reported for other vertebrates, it
becomes apparent that maneuvering lovebirds shift their gaze faster than other animals serving
as visual model systems (vertebrate review: [14], cat: [32], rabbit: [33] chameleon: [58], goldfish: [59], frog: [60], zebrafish: [61], primates: [62]) (Fig 5A). For example, visually induced
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Fig 8. Lovebirds improve visual flight control by coordinating super-fast gaze shifts with the end of their downstroke. (A) Top view schematic of a
typical recording that shows that the wings occlude the lateral visual field at the end of the downstroke. The lovebird’s azimuthal visual field is approximated
from ophthalmologic measurements at the visual equator of Senegal parrots [57]. (B) For demonstrative purposes a lovebird was filmed from the side during
a turning on a dime maneuver with the side panels of the arena removed (this video sequence is not part of the data analysis). (C) Most saccades were
initiated at 75% of the downstroke (see Fig 3B), when the wings occlude more than half of the lateral visual field.
gaze shifts in humans, which are among the fastest in vertebrates, reach up to 500°/s when
head and eye movements are combined [63] and around 400°/s when only the eyes are moved
[31]. Lovebirds are on average twice as fast at about 930°/s. Fast gaze shifts reduce the time during which visual information processing might be impaired due to motion blur and, thus, prolong the net time during which visual information can be obtained. The fact that birds also
have relatively fast flicker fusion frequencies above 100 Hz, and thus, a higher motion sensitivity compared to other vertebrates [64,65], further shows that birds have evolved extraordinary
visual capabilities. This high-performance visual and gaze control is essential to guide fast flight
through cluttered habitats.
Control of head-neck motion during saccadic and intersaccadic phases
The rapid horizontal gaze shifts and the gaze stabilization between saccades are facilitated by
the sophisticated avian head-neck system and its sensory motor control [66]. The avian neck is
built up by more than twice as many vertebrae compared to a mammal, and is actuated by over
200 muscles on each side. These muscles can be classified in five functional groups from which
the first, the carnio-cervical and the fourth, the lateral system appear to be most relevant for
horizontal head rotations relative to the body [43]. Both muscle groups contain strongly pinnate mono-articular muscles actuating motion around several vertebral joints at once. Their
muscle fibers are very long which allows especially fast contractions [43,67,68]. This specialized
musculoskeletal system and the relatively light head enables birds to make rapid precisely coordinated head turns. Indeed, the median saccade duration in lovebirds is just 29.6 ms, which
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corresponds to a contraction time as fast as flight muscle contraction (a lovebird downstroke
takes about 30 ms).
Saccadic head turn. How may the here reported saccadic head turns be controlled?
There are two well-studied gaze behaviors that induce rapid head turns in space. The first behavior is the reflexive fast phase of the optocollic reflex (OCR), of which the function is interpreted to be similar to the fast resetting of the eyes; the optokinetic nystagmus (OKN). Brain
systems involved in the control of both reflexes are the nucleus of the basal optic roots
(nBOR), the pretectal nucleus lentiformis mesencephalic (LM) and the vestibule-cerebellum
[69]. The second behavior facilitates voluntary saccadic gaze changes towards a visual feature
[14,66]. As the LM and nBOR are involved in controlling horizontal eye saccades [70,71],
they are also likely candidates to control visually induced horizontal head saccades. The headbody relation we observed (i.e. Fig 2B green line) are slightly different from the classical OCR
response observed in restrained animals that stabilize a moving pattern [72]. Although, head
and body turn in the same direction during the turn on a dime maneuver, turning lovebirds
increase the deviation angle between head and body with their saccades. This observation is
similar to head motions in maneuvering zebra finches [3] and blowflies [27]. In contrast,
body-restrained birds make saccades directed to the body to re-center their head orientation
while stabilizing a pattern on their retina [72]. Our observation that lovebirds turn their head
from normal orientation towards salient visual features, such as the perch edge and its center,
indicates that a visual function is most parsimonious (example saccades 2 and 3 in Fig 2E and
quantitative results in Fig 6). Similar saccadic eye-in-head traces have been described for primates performing a series of eye saccades during a visual fixation task with continuous head
rotation (Fig 6 in [62]).
Head stabilization between saccades. Between saccadic gaze changes, rotational head stabilization in space is likely coordinated by the vestibulo-collic reflex (VCR) [62,66]. Visual control in the form of the optocollic reflex (OCR) may increase rotational head stability between
saccades even further. In addition to possible sensory functions, the reflexive head stabilization
in space promotes head-body realignment, which is mediated by the continuously turning
body. Interestingly, the velocity tuning of the OCR and VCR change with the activity state of
birds [73–76]. The exact roles and integration of the vestibular and visual system during free
flight are, however, not well understood.
Gaze approximation based on head orientation
To understand how the observed gaze behavior is used to fixate image features we need information about head stabilization, eye motion, and the actual focus of visual attention on the retina. So far only head orientation data has been obtained in freely flying birds [3,10,44,51,77–
80], eye orientation and/or focus of attention have only been measured in head fixed [1,57,81–
83], body restrained [15,84], and walking [85] birds. To interpret how the observed head stabilization behaviors in free flight might facilitate image stabilization we assume eye motion is
small in flight. This is supported by the observation that birds predominately change gaze by
turning their heads (review [14]). In contrast, humans prefer to facilitate gaze shifts of up to
45° with eye movements [63], which are of similar amplitude as the head saccades we report
here for lovebirds. In head unrestrained pigeons, gaze stabilization to horizontal wide field motion is facilitated by a combination of optokinetic and optocollic reflexes. Image stabilizing
head rotations account for 80–90% of the total gaze shifts. However, when restraining the
head, eye movements rose to 90% of the total gaze shift [13,86], this supports our assumption
that head-free birds coordinate gaze orientation through head saccades. Indeed, studies quantifying the amount of eye movements in unrestrained pigeons, chickens, and peahens found that
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horizontal eye movements are typically below 10°, much smaller than the anatomical range
[15,16,85].
Whereas we can assume that eye orientation is coordinated with head saccades in free flight,
we do not have retinal morphological data to determine how head orientation relates to fovea
orientation. Temporal regions with higher visual acuity that are frontally oriented like in cockatoos [87] seem to be absent in parrots [57,88]. However, the role of foveation in flight has not
been established. Thus, free flight foveation experiments are needed to assess if maneuvering
birds fixate salient visual features frontally with their retinal regions of high visual acuity.
Which visual parameters help guide landing on a swinging perch?
By scrutinizing the high-speed flight recordings, we found that the first behavior indicating
landing is a downward pitch of the tail feathers that resulted in an upward pitched body posture accompanied by an extension of the legs. Therefore, and because the exact timing of leg extension was difficult to assess from our videos, we based our definition of landing initiation on
the tail pitch behavior. The tail pitch analysis starts up to 50–100 ms earlier than the previous
analysis based on leg extension ([4,5]; we compared tail pitch and visible leg motion timing in
our recordings). We find that retinal size is most parsimonious to initiate a downward tail
pitch in lovebirds when they land on a swinging perch, as has been proposed for pigeons landing on a static perch [5]. For a perch of known size, its retinal expansion can be a direct measure for its distance. We did not observe head-bobbing behavior as reported for landing
pigeons [79]. In contrast to pigeons and insects landing on a plane, we found no clear monotonic dependence of flight velocity reduction and retinal expansion speed (correlation coefficient of both parameters: r = 0.58, n = 16 flights, N = 5), or rate of change of retinal expansion
speed (correlation coefficient of both parameters: r = 0.45, n = 16 flights, N = 5) [5,40]. On
some occasions, the relative flight velocity even increased for increasing retinal expansion
speed, because the perch started swinging towards the bird (green trace in Fig 7B). To see how
the sampling rate of visual cues affected the calculation of temporal derivative based parameters like the retinal expansion speed, RREV, tau and their respective c.v. values, we down sampled our dataset from 2000 Hz to 50 Hz (as used in pigeon studies [4,5]). Interestingly, we
found that c.v. values changed minimally but the general order of parameter variability did not
change at lower temporal resolutions. Retinal perch size had consistently the lowest c.v. values.
However, at both temporal resolutions, we saw a variability drop of RREV around 80 ms before
landing. Consequently, our data suggests that retinal size is used as a cue for coordinating the
final phase of landing on a moving perch. Further, the data indicates that in early phases before
landing the RREV could as well be used as reported for insects [37,39].
Stabilization of features and high-contrast edges improves optic flow
The finding that lovebirds stabilize wall and perch edges before and during turning, and the
perch center after turning to coordinate their landing, may indicate that birds and humans use
similar features for navigation. Edges of obstacles that need to be avoided (the arena walls) are
visually stabilized by lovebirds, similar to the center of the visual target they choose to approach
(the perch) [11]. Such a gaze strategy is thought to facilitate optic flow based navigation [12].
According to the theory, stabilization of obstacle edges results in strong optic flow discontinuities of obstacle and background, which improves experienced visual scene segmentation—a
key feature for path planning [12]. Frontal feature stabilization may help birds to reduce distance-independent rotational image flow and, thus, support self-induced motion analysis
[49,50,89]. Moreover, the target image expands symmetrically when a visual goal is stabilized
in heading direction, which simplifies proximity computation. The amount of image expansion
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depends on feature distance and approach speed [7], and might therefore be involved in the
control of the goal approach—landing in our case. This theory is supported by our landing
analysis. We found that 30 ms to 85 ms before landing initiation (downward tail pitch) the
perch size (α) varied least. In addition, relative retinal expansion velocity (RREV) varied second least. Consequently, both variables are plausible factors for initiating landing [5,37,39].
The size of the swinging perch (α) is, however, the most parsimonious control parameter in
our study because it varied least.
Tuned gaze shifts for increased visual awareness
Flapping animals frequently conduct subtle adjustments to their wing movements, body posture, and head orientation to stabilize themselves [90]. Our observation that lovebirds time the
start of the saccade at 75% of the downstroke can be understood as a strategy to maximize visual awareness. At 75% of the downstroke the wings are in a position lateral to the head and occlude the lateral field of view (Fig 8); lovebirds thus facilitate visual perception by overlying
behaviors that impair vision. This finding complements the reports that birds are specifically
motion sensitive in the lateral field of view [91] and depend on this input for visuomotor control [2]. The minimal delay associated with avian visuomotor control is about 30 ms [42]. Considering the birds’ wingbeat lasts 60 ms and that saccades were initiated at about 23 ms into the
downstroke, it is unlikely that visual information obtained during the same downstroke triggers
the saccadic gaze shift. Instead, the most parsimonious interpretation of the gaze-shift behavior
is that translational image motion obtained in the previous wingbeat(s) triggers the saccade.
This has been found for insects during collision avoidance [92–94]. For insects it is, however,
unknown if they time their saccade as exquisitely within a wingbeat as lovebirds do. In contrast
to the head saccade, we found that the tail pitch is not precisely coordinated with the wingbeat.
This emphasizes that saccadic gaze changes are precisely tuned with the wingbeat, whereas
some other motor commands related to flight control are not. The super-fast gaze behaviors
we describe here for lovebirds can aid vision-based autopilots for robots flying in cluttered and
GPS-denied environments.
Supporting Information
S1 Movie. Slow motion video of a lovebird performing a turning on a dime maneuver. The
bird performed a saccadic gaze behavior timed within the wingbeat. Between saccadic turns,
the head is rotationally stabilized. In contrast, the body turns continuously during the maneuver following the head. The bottom graph illustrates the estimated head and body yaw orientation relative to a horizontal in the arena. An orientation of 0° represents a straight flight away
from the perch, 180° a flight to the right towards the perch. Vertical bars indicate the downstroke phases and thereby the wing beat timing. The video is 50x slowed down for illustration.
The actual maneuver took 1.15 seconds. See Fig 2 for more quantitative results.
(MP4)
Acknowledgments
We thank Monica Caravias, Harine Ravichandiran and Michal Wegrzynski for their help in digitizing the high-speed footage. We thank Eric Karruppannan for building the setup and Johan van
Leeuwen for supporting the DEC protocol. Further, we would like to thank Douglas Altshuler,
Benny Goller, Ingo Schiffner and Mandyam Srinivasan for fruitful discussions of the results, and
Ashley Pete for proofreading the manuscript. DK was supported by HFSP grant RGP0003/2013,
DL was supported by HFSP grant RGP0003/2013 and ONR MURI grant N00014-10-1-0951.
20 / 24
Author Contributions
Conceived and designed the experiments: DL. Performed the experiments: DL. Analyzed the
data: DK EvB DL. Contributed reagents/materials/analysis tools: DK EvB DL. Wrote the paper:
DK EvB DL.
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How Lovebirds Maneuver Rapidly Using Super-Fast

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